2023/03/30 by Lorenzo Maggi, Ryo Koblitz, Maggi, Lorenzo +5 · 1 citation
Engineering · #Advanced MIMO Systems Optimization #FOS: Computer and information sciences #Information Theory (cs.IT) #Millimeter-Wave Propagation and Modeling #Power Line Communications and Noise
paper · pdf · doi:10.48550/arxiv.2303.17301
openalex publication_date 2023/03/30 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
The standard beam management procedure in 5G requires the user equipment (UE) to periodically measure the received signal reference power (RSRP) on each of a set of beams proposed by the basestation (BS). It is prohibitively expensive to measure the RSRP on all beams and so the BS should propose a beamset that is large enough to allow a high-RSRP beam to be identified, but small enough to prevent excessive reporting overhead. Moreover, the beamset should evolve over time according to UE mobility. We address this fundamental performance/overhead trade-off via a Bayesian optimization technique that requires no or little training on historical data and is rooted on a low complexity algorithm for the beamset choice with theoretical guarantees. We show the benefits of our approach on 3GPP compliant simulation scenarios.